Supplementing biometric identification with device identification

ABSTRACT

A computer may identify an individual according to one or more biometrics based on various physiological aspects of the individual, such as metrics of various features of the face, gait, fingerprint, or voice of the individual. However, biometrics are often computationally intensive to compute, inaccurate, and unable to scale to identify an individual among a large set of known individuals. Therefore, the biometric identification of an individual may be supplemented by identifying one or more devices associated with the individual (e.g., a mobile phone, a vehicle driven by the individual, or an implanted medical device). When an individual is registered for identification, various device identifiers of devices associated with the individual may be stored along with the biometrics of the individual. Individuals may then be identified using both biometrics and detected device identifiers, thereby improving the efficiency, speed, accuracy, and scalability of the identification.

BACKGROUND

Within the field of computing, many scenarios involve an identificationof an individual using one or more biometrics. As a first example, acamera may capture an image or video recording of the individual, mayevaluate various visible aspects of the individual (e.g., facialfeatures, body shape, and gait), and may generate a set of visiblebiometrics that represent the individual. As a second example, amicrophone may capture and evaluate the voice of the individual, andvarious biometrics may be identified based on the acoustic properties ofthe voice (e.g., pitch, timbre, and rate of speech). As a third example,a fingerprint scanner may capture and evaluate a fingerprint of theindividual, and biometrics relating to the pattern of ridges and whorlsof the fingerprint may be identified. These analyses may be initiallyperformed to capture one or more biometrics identifying the individual,and may be stored, e.g., in a biometric database associating thebiometrics with an individual identity of the individual. Later, when anunidentified individual is detected, various biometrics may be capturedand compared with those in the biometric database to identify theindividual. Such capturing and identification may involve multiplebiometrics (either of the same modality, e.g., multiple biometricmeasurements of the face of the individual, or of different modalities,e.g., a facial feature, a fingerprint, and a voiceprint of theindividual) in order to improve the accuracy of the identification.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key factors oressential features of the claimed subject matter, nor is it intended tobe used to limit the scope of the claimed subject matter.

Identifying individuals using biometrics may be difficult or inefficientin several respects. As a first example, the capturing and evaluation ofthe biometrics may comprise resource-intensive processes, therebyutilizing a high number of computing resources (e.g., power, processor,storage, memory, and/or network capacity). The resource utilization mayincur a significant delay in performing the biometric analyses, and/ormay be difficult to implement on computing devices having limitedresources. As a second example, biometric evaluation may not beadequately robust, and may produce different results in differentcircumstances (e.g., different biometrics may be generated from the faceof an individual if the visual lighting of the scene changes; if theangle of the captured image of the face changes; if the individual makesa different facial expression during capturing; or if the face of theindividual changes, such as the use of different make-up, a differenthair color or style, or an acquired physical deformity). These newcircumstances may result in false negatives (e.g., failing to recognizea known individual) and/or false positives (e.g., incorrectlyidentifying an unknown individual as a known individual, or confusing afirst known individual with a second known individual). Moreover, theseinefficiencies may be exacerbated as the biometrics system scales toidentify hundreds or thousands of individuals; e.g., if a particularbiometric is stored as a hash value, the probability of a hash collisionamong two or more individuals may grow at an unacceptable rate.

Presented herein are techniques for supplementing the identification ofbiometrics with other information about an individual that may be moreefficiently and/or accurately identified. It may be appreciated that inmany contemporary scenarios, individuals often carry one or more devicesthat may be identifiable by the computer performing the biometricidentification. For example, an individual may regularly carry aparticular mobile phone, a pager, a mobile computer (such as a palmtop,laptop, or tablet computer), a gaming device, a camera, an audio orvideo player, or an implanted medical device (e.g., a pacemaker). Thecomputer may be able to detect an identifier of the device, where thedevice identifier distinctively identifies the device (e.g., anidentifier that identifies only one device) or differentiates the devicefrom other devices that may be carried by other individuals (e.g., aparticular model of a mobile phone). The computer may thereforeassociate one or more devices with an individual who carries thedevices, and may, while registering the individual identity of anindividual (e.g., while creating a user account in a user database),associate the device identifier of a detected device with the individualidentity. Subsequently, when the computer seeks to identify anunidentified individual, the computer may, in addition to identifyingone or more biometrics of the individual, detect one or more devicescarried by the individual, and may detect one or more device identifiersof the respective devices (e.g., a device ID of a mobile phone, such asa mobile phone number, and/or a device ID of an implanted medicaldevice, such as a pacemaker). The computer may then search the biometricdatabase for individuals associated both with the detected biometricsand with the detected device identifiers. For example, the detecteddevice identifiers may be utilized to reduce the number of individualidentities that may match the biometrics of the individual. Thecapturing, storage, and retrieval of device identifiers may thereforesupplement the identification of the individual according to variousbiometrics.

The inclusion of a device identifier to supplement the identification ofan individual using biometrics may present some advantages over usingonly biometrics. As a first example, if the set of individuals who maybe identified via biometrics is potentially large, the set ofindividuals potentially matching an unknown individual may besignificantly reduced based on the device identifier (e.g., of a largeset of known individuals, only a small number of such individuals may beknown to carry a particular device type, so when an individual carryingthe device type is presented, the number of individuals to be consideredfor biometric identification may be significantly reduced), therebyincreasing the efficiency, speed, accuracy, and scalability of theidentification. As a second example, the identification of a device mayoften be achieved with fewer computing resources than identifying abiometric, thereby providing an additional piece of evidence of theidentity of the individual with a comparatively lower use of computingresources

To the accomplishment of the foregoing and related ends, the followingdescription and annexed drawings set forth certain illustrative aspectsand implementations. These are indicative of but a few of the variousways in which one or more aspects may be employed. Other aspects,advantages, and novel features of the disclosure will become apparentfrom the following detailed description when considered in conjunctionwith the annexed drawings.

DESCRIPTION OF THE DRAWINGS

FIG. 1 is an illustration of an exemplary scenario featuring anidentification of an individual using a set of biometrics.

FIG. 2 is an illustration of an exemplary scenario featuring anidentification of an individual using a biometric and a deviceidentification of a device associated with the individual according tothe techniques presented herein.

FIG. 3 is a flow chart illustrating an exemplary method of identifyingindividuals.

FIG. 4 is a component block diagram illustrating an exemplary system foridentifying individuals.

FIG. 5 is an illustration of an exemplary computer-readable mediumcomprising processor-executable instructions configured to embody one ormore of the provisions set forth herein.

FIG. 6 is an illustration of an exemplary scenario featuring aregistration of an individual identity of an individual.

FIG. 7 is an illustration of an exemplary scenario featuring apersistent presence tracking of a group of individuals.

FIG. 8 illustrates an exemplary computing environment wherein one ormore of the provisions set forth herein may be implemented.

DETAILED DESCRIPTION

The claimed subject matter is now described with reference to thedrawings, wherein like reference numerals are used to refer to likeelements throughout. In the following description, for purposes ofexplanation, numerous specific details are set forth in order to providea thorough understanding of the claimed subject matter. It may beevident, however, that the claimed subject matter may be practicedwithout these specific details. In other instances, structures anddevices are shown in block diagram form in order to facilitatedescribing the claimed subject matter.

Within the field of computing, many scenarios involve an identificationof an individual using a set of one or more biometrics. For variousindividuals, a computer may register an individual identity by capturingmeasurements of various physical properties of the individual. When thecomputer is later presented with an individual, the computer may thenperform the same types of measurements on the individual, and mayidentify the individual by identifying an individual identity having thesame measurements.

As a first example, a camera may capture an image or video recording ofan individual, and may evaluate various visible aspects of theindividual (e.g., facial features, such as the distance between the eyesand the width of the nose; body shape, such as height and build; andgait, such as stride length, cadence, and arm swing). These measurementsmay form a set of biometrics that represent various visible aspects ofthe individual. As a second example, a microphone may capture andevaluate the voice of the individual. Various acoustic properties of thevoice of the individual may be measured (e.g., pitch, timbre, and rateof speech), and may be utilized as biometrics based on the voice of theindividual. As a third example, an optical or capacitative fingerprintscanner may detect various properties of a fingerprint of theindividual, such as the depth of ridges, locations and sizes of whorls,and lengths of valleys. When initially presented with an individual, acomputer may capture one or more biometrics identifying the individual,and may store such biometrics, e.g., in a biometric database comprisingone or more individual identities that respectively represent anindividual based on a set of biometrics. Some such computers may capturemultiple biometrics for an individual (either of the same modality,e.g., multiple biometric measurements of the face of the individual, orof different modalities, e.g., a facial feature, a fingerprint, and avoiceprint of the individual). When an unidentified individual isdetected, the computer may then capture various biometrics of theindividual and compare such biometrics with those in the biometricdatabase, and may select an individual identity matching some or all ofthe biometrics.

FIG. 1 presents an illustration of an exemplary scenario 10 featuring acomputer 12 configured to identify various individuals 14 according toone or more biometrics 18. In this exemplary scenario 10, the computer12 comprises a set of biometric detectors 22, including a camera, amicrophone, and a fingerprint scanner. These biometric detectors 22(optionally including one or more software algorithms used inconjunction therewith) may be configured to capture a representation 24of a physiological aspect of an individual 14, and to perform ananalysis 26 on the representation 24 in order to generate one or morebiometrics 18. When an individual 14 is first presented to the computer12, the biometric detectors 22 may capture a representation 24 of thephysiological aspect of the individual 14 (e.g., a still image of theface of the individual 14, an audio recording of a voice sample of theindividual 14, and a fingerprint scan of a finger or thumb of theindividual 14), and for each representation 24, may perform an analysis26 to identify one more biometrics 18. For example, the camera maygenerate biometrics 18 associated with various visual measurements anddimensions of the face of the individual 14; the microphone may generatebiometrics 18 associated with various acoustic measurements of the voiceof the individual 14; and the fingerprint scanner may generatebiometrics 18 associated with various patterns detected in thefingerprint of the individual 14. The computer 12 may also comprise anindividual identity set 20, comprising a set of individual identities 16for respective individuals 14 that store various biometrics 18identifying the individual 14. For example, the various features of therepresentation 24 detected during the analysis 26 may be translated intoa set of integers or a string, from which a hashcode may be computedusing a hashing algorithm, and this hashcode (comprising a biometric 18)may be stored in the individual identity 16 of the individual 14.Subsequently, when an individual 14 is presented to the computer 12(e.g., by stepping in front of the camera), the computer 12 may endeavorto identify the individual 14 by invoking the biometric detectors 22 todetect the same types of biometrics 18, and may compare the detectedbiometrics 18 of the individual 14 with those stored in variousindividual identities 16 of the individual identity set 20. If theindividual 14 exhibits similar physiological features during the initialregistration and the subsequent identification, the computer 12 maytherefore be able to identify the individual 14 on the basis ofbiometrics 18 calculated from measurements of these physiologicalfeatures. Additionally, this identification may be performed without theinvolvement of the individual 14; e.g., the individual 14 does not haveto interact with the computer 12, such as by using a keyboard or otherinput device to enter identifying credentials. This less intrusivesystem may be more convenient for the individual 14, and/or may beperformed without the knowledge or cooperation of the individual 14, andpossibly even at a large distance (e.g., cameras positioned far awayfrom the individual 14 but having significant zoom capabilities).

However, the exemplary scenario 10 of FIG. 1 may result in someinefficiencies and/or disadvantages. As a first example, the analysis 26of representations 24 to identify biometrics 18 may be computationallyintensive, and may consume a significant amount of power, processor,memory, storage, and/or network capacity. For example, identifyingbiometrics 18 from the face of an individual 14 may involve ahigh-resolution image captured with a camera, the application ofsophisticated machine vision algorithms to identify the location of theface in the image and the features thereof, and the computation ofbiometrics 18 (such as hashcodes using various hash algorithms).Additionally, the computational resources involved in identifying alarge number of individuals may rapidly increase, and may steadilyreduce the perform ant nature of the biometric identification as a largenumber of individuals are registered. More computationally powerfulcomputers 12 may be capable of performing this analysis 26 for one orseveral biometrics 18, but at a significant cost of resources, andpossibly with a noticeable delay in identifying the individual 14; andless computationally powerful computers 12 may have to scale back to amore rudimentary identification of biometrics 18 (e.g., alower-resolution image or less sophisticated machine vision algorithms)that may be less accurate, or may simply be incapable of performing theanalysis 26.

As a second example, biometrics 18 may often be inaccurate. For example,the biometrics 18 identified for the face of an individual 14 may changeif the face of the individual 14 is image in a different light or at adifferent angle, if the individual 14 makes a different facialexpression (such as smiling), if the individual appears different due tothe use of make-up or a change of hairstyle, or if the individualsuffers a facial deformity. Similarly, the biometrics 18 identified fromthe gait of an individual 14 or from a fingerprint of the individual 14may change if the individual 14 walks differently or intentionally oraccidentally damages the skin of the finger. The use of multiplebiometrics 18 may alleviate some of these changes (e.g., capturing threedifferent biometrics 18 may allow a reliable identification of theindividual 14 even if one biometric 18 changes), but further increasesthe computational costs of the biometric identification.

As a third example, individual identification via biometrics 18 may notscale well to match the identification of a large number of individuals14. For example, the complexity and inaccuracy of the identificationsystem may increase in proportion with the number of individuals 14 whoare registered with the identification system. Identifying a particularindividual 14 among a large set of known individuals 14 may thereforeinvolve a greater number, variety, and precision of measurements areincreasingly sensitive to smaller changes in the physiology of theindividual 14. This expansion may increase the costs, complexity, delay,and/or inaccuracy of the identification system, thereby limiting itsscalability when utilized to identify a potentially large number ofindividuals 14.

Due to these problems, it may be appreciated that systems that identifyindividuals 14 based solely on biometrics 18 may be inefficient orinadequate in some scenarios. However, some of these disadvantages maybe alleviated by utilizing other information about an individual 14 thatmay supplement the biometric identification by providing additionalinformation as to the identity of an individual. Additionally, it may bedesirable to collect information that is comparatively easy to obtain(e.g., without involving computationally intensive analyses 26) and/orthat may be collected without the involvement (and perhaps without theknowledge and/or cooperation) of the individual 14.

One such piece of information relates to devices that an individual 14may carry. In many contemporary scenarios, individuals 14 often carryone or more devices, such as a mobile phone, a pager, a mobile computer(such as a palmtop, laptop, or tablet computer), a global positioningsystem (GPS) receiver, a gaming device, a camera, an audio or videoplayer, or an implanted medical device (e.g., a pacemaker or cochlearimplant). Such devices may even include devices lacking an independentpower source, such as a radiofrequency identifier (RFID) chip embeddedin an identification card such as a bank card or credit card, asolid-state data storage chip, or a Subscriber Identity Module (SIM)card inserted in a mobile phone. The individual 14 may have a knownassociation with such a device, such as a regular habit of carrying thedevice. The computer 12 may be able to identify a device in theproximity of the individual 14 (either precisely (e.g., the devicehaving a distinctive identifier); as a particular device type (e.g., aparticular model, such as a model or brand of a mobile phone); or as adevice class (e.g., simply a mobile phone of an unidentified modeltype).) This information alone may be inadequate for identifying theindividual 14. For example, a device detector 40 may only be able toidentify the location of a device 34 (and, presumably, the individual 14carrying it) within a general area, e.g., a one-hundred-meter radiusaround a WiFi receiver, while a biometric detector 22 may identify thelocation of the individual 14 with much greater precision. However, thedetection of the device 34 may be useful for supplementing the biometricidentification of the individual 14. For example, this information maybe used to reduce the number of individual identities 16 of theindividual identity set 20 to those that are associated with thedetected device, and the computer 12 may be able to perform lesssearching, and/or to collect a fewer number of biometrics 18 in order tocomplete the identification of the individual 14, thereby resulting in afaster and/or more efficient identification of the individual 14.

The identification of a device associated with an individual may alsohave other advantages. For example, the identification of a deviceassociated with the individual may be used to improve the accuracy ofthe identification of the individual 14, such as by resolving conflictsamong the collected biometrics 18 (e.g., where an individual 14 matchesonly two out of three biometrics 18 comprising an individual identity16, the identification of a device associated with the individualidentity 16 and detected in the proximity of the individual 14 mayresolve the uncertainty), and/or to resolve conflicts among individualidentities 16 (e.g., in the case of a hash collision where a biometric18 identified for an individual 14 is associated with two individualidentities 16, the detection of the device that is associated with onlyone of the two individual identities 16 may facilitate a resolution ofthe hash collision). The use of the device identification may thereforeimprove the efficiency, speed, accuracy, and/or scalability of thebiometric identification system utilized by the computer 12.

FIG. 2 presents an illustration of an exemplary scenario 30 featuring anidentification of a device to supplement the biometric identification ofan individual 14. In this exemplary scenario 30, a computer 12comprising a biometric detector 22 (e.g., a camera) is configured toidentify an individual 14 by capturing a representation 24 of aphysiological aspect of the individual 14 (e.g., the face of theindividual 14) and performing an analysis 26 to identify one or morebiometrics 18 that may later be used to identify the individual 14.However, in accordance with the techniques presented herein, thecomputer 14 also includes a device detector 40, such as a wired orwireless communication component, that is configured to detect a device34 associated with the individual 14 (e.g., by communicating with awireless transmitter component 38 of the device 34). When the individual14 is initially presented to the computer 12, the computer 12 mayregister the individual 14 by identifying one or more biometrics 18, butmay also invoke the device detector 40 to detect one or more devices 34having an association 32 with the individual 14. In particular, thedevice detector 40 may identify one or more device identifiers 36, suchas a distinctive identifier or an identification of the device type ordevice class of the device 34. The computer 12 may then store in anindividual identity set 20 an individual identity 16 for the individual14, comprising both the at least one biometric 18 and the at least onedevice identifier 36. Subsequently, when an unidentified individual 14is presented to the computer 12, the computer may invoke the biometricdetector 22 to identify one or more biometrics 18 of the individual 14,and may concurrently or consecutively invoke the device detector 40 toidentify one or more device identifiers 36 of at least one device 34 inthe proximity of the individual 14. The computer 12 may then examine theindividual identities 16 of the individual identity set 20 to identifyan individual identity 16 comprising the biometric 18 and also thedevice identifier 36. In this manner, the computer 12 may utilize thedevice identifier 16 to supplement the identification of the individual14 based on the detected set of biometrics 18.

FIG. 3 presents a first embodiment of these techniques, illustrated asan exemplary method 50 of identifying individuals 14 using a computer 12having a processor, a data store, a biometric detector 22, and a devicedetector 40. The exemplary method 50 may be implemented, e.g., as a setof software instructions stored in a memory component (e.g., a systemmemory circuit, a platter of a hard disk drive, a solid state storagedevice, or a magnetic or optical disc) of the computer 12, that, whenexecuted by the processor of the computer 12, cause the processor toperform the techniques presented herein. The exemplary method 50 beginsat 52 and involves executing 54 the instructions on the processor. Morespecifically, the instructions are configured to, for respectiveindividuals 14, store 56 in the data store an individual identity 16comprising at least one biometric 18 identifying the individual 14, andat least one device identifier 36 identifying at least one device 34associated with the individual 18. The instructions are also configuredto identify an individual 14 who is detectable by the computer 12 by,using the biometric detector 22, detecting 60 at least one biometric 18identifying the individual 14; using the device detector 62, detecting62 at least one device identifier 36 identifying at least one device 34that is associated with the individual 14; and retrieving 64 from thedata store an individual identity 16 comprising the biometric 18 and thedevice identifier 36. In this manner, the instructions cause thecomputer 12 to identify individuals 14 according to the techniquespresented herein, and so the exemplary method 50 ends at 66.

FIG. 4 presents a second embodiment of these techniques, illustrated asan exemplary system 76 configured to identify individuals 14 using acomputer 72 having a processor 74, a biometric detector 22, and a devicedetector 40. The exemplary system 76 may be implemented, e.g., as asoftware architecture, comprising a set of components, each comprising aset of software instructions stored in a memory component (e.g., asystem memory circuit, a platter of a hard disk drive, a solid statestorage device, or a magnetic or optical disc) of the computer 72, that,when executed (concurrently or consecutively) by the processor of thecomputer 72, cause the processor 74 to perform one or more tasks of thetechniques presented herein. The exemplary system 76 includes a datastore 78 that is configured to store individual identities 16 ofrespective individuals 14, where each individual identity 16 comprisesat least one biometric 16 identifying the individual 14 (e.g., accordingto an identified physiological aspect of the individual 14), and atleast one device identifier 36 identifying at least one device 34associated with the individual 14. The exemplary system 76 also includesan individual identity generating component 80, which is configured tostore in the data store 78 individual identities 16 of respectiveindividuals 14 by, using the biometric detector 22, detecting at leastone biometric 18 identifying the individual 14; using the devicedetector 40, detecting at least one device identifier 36 identifying atleast one device 34 associated with the individual 14; and storing inthe data store 78 an individual identity 16 comprising the biometric 18and the device identifier 36. The exemplary system 76 also includes anindividual identifying component 82, which is configured to identify anindividual 14 by, using the biometric detector 80, detecting at leastone biometric 18 identifying the individual 14; using the devicedetector 40, detecting at least one device identifier 36 identifying atleast one device 34 that is associated with the individual 14; andretrieving from the data store 78 an individual identity 16 comprisingthe biometric 18 and the device identifier 36. In this manner, thecomponents of the exemplary system 76 in the exemplary scenario 70 ofFIG. 4 cause the processor 74 to identify individuals 14 according tothe techniques presented herein.

Still another embodiment involves a computer-readable medium comprisingprocessor-executable instructions configured to apply the techniquespresented herein. Such computer-readable media may include, e.g.,computer-readable storage media involving a tangible device, such as amemory semiconductor (e.g., a semiconductor utilizing static randomaccess memory (SRAM), dynamic random access memory (DRAM), and/orsynchronous dynamic random access memory (SDRAM) technologies), aplatter of a hard disk drive, a flash memory device, or a magnetic oroptical disc (such as a CD-R, DVD-R, or floppy disc), encoding a set ofcomputer-readable instructions that, when executed by a processor of adevice, cause the device to implement the techniques presented herein.Such computer-readable media may also include (as a class oftechnologies that are distinct from computer-readable storage media)various types of communications media, such as a signal that may bepropagated through various physical phenomena (e.g., an electromagneticsignal, a sound wave signal, or an optical signal) and in various wiredscenarios (e.g., via an Ethernet or fiber optic cable) and/or wirelessscenarios (e.g., a wireless local area network (WLAN) such as WiFi, apersonal area network (PAN) such as Bluetooth, or a cellular or radionetwork), and which encodes a set of computer-readable instructionsthat, when executed by a processor of a device, cause the device toimplement the techniques presented herein.

An exemplary computer-readable medium that may be devised in these waysis illustrated in FIG. 5, wherein the implementation 90 comprises acomputer-readable medium 92 (e.g., a CD-R, DVD-R, or a platter of a harddisk drive), on which is encoded computer-readable data 94. Thiscomputer-readable data 94 in turn comprises a set of computerinstructions 96 configured to operate according to the principles setforth herein. In one such embodiment, the processor-executableinstructions 96 may be configured to perform a method of identifyingindividuals, such as the exemplary method 50 of FIG. 3. In another suchembodiment, the processor-executable instructions 96 may be configuredto implement a system for identifying individuals, such as the exemplarysystem 76 of FIG. 4. Some embodiments of this computer-readable mediummay comprise a non-transitory computer-readable storage medium (e.g., ahard disk drive, an optical disc, or a flash memory device) that isconfigured to store processor-executable instructions configured in thismanner. Many such computer-readable media may be devised by those ofordinary skill in the art that are configured to operate in accordancewith the techniques presented herein.

The techniques discussed herein may be devised with variations in manyaspects, and some variations may present additional advantages and/orreduce disadvantages with respect to other variations of these and othertechniques. Moreover, some variations may be implemented in combination,and some combinations may feature additional advantages and/or reduceddisadvantages through synergistic cooperation. The variations may beincorporated in various embodiments (e.g., the exemplary method 50 ofFIG. 3 and the exemplary system 76 of FIG. 4) to confer individualand/or synergistic advantages upon such embodiments.

A first aspect that may vary among embodiments of these techniquesrelates to the scenarios wherein such techniques may be utilized. As afirst variation of this first aspect, these techniques may be utilizedto track many types of biometrics 18 using many types of biometricdetectors 22, such as cameras, microphones, tactile sensors, andchemical analyzers, as well as many types of algorithms, such as machinevision algorithms, acoustic analysis algorithms, and machine learningalgorithms that are developed and/or trained to perform tasks such aspattern recognition. For example, the biometric detector 22 may comprisea camera configured with a face detection component (e.g., an algorithmconfigured to analyze an image, identify an area of the image depictinga face of an individual, and extract biometrics 18 from the detectedface), and the biometrics 18 identifying the individual 14 may compriseface biometric measurements.

As a second variation of this first aspect, these techniques may beutilized to track many types of devices 34 using many types of devicedetectors 40. As a first example the device detector 40 may comprisemany types of wireless or wireless communication adapters (e.g.,cellular network adapters, local area network adapters, and wide areanetwork adapters), or even a camera configured to visually identify thedevice 34 (and possibly the same camera used to detect the biometric 18of the individual 14). While this latter embodiment may be lessefficient than others (e.g., by involving an analysis 26 of arepresentation 24 of the device 34, such as an image), it may haveparticular advantages, e.g., by facilitating the detection of a device34 that is not capable of or configured to communicate with the devicedetector 40). As a second example, these types of device detectors 40may also be capable of detecting many types of devices 34, includingmobile phones, pagers, mobile computer (such as a palmtop, laptop, ortablet computer), global positioning system (GPS) receivers, gamingdevices, cameras, audio or video players, implanted medical devices(e.g., a pacemaker or cochlear implant), and even devices lacking anindependent power source, such as radiofrequency identifier (RFID) chipsembedded in an identification card such as a bank card or credit card,solid-state data storage chips, and Subscriber Identity Module (SIM)cards inserted in a mobile phone. As a third example, many types ofdevice identifiers 36 may be identified with respect to such devices 34,including a distinctive identifier (e.g., a Globally Unique Identifier(GUID)) that definitively differentiates the device 34 from every otherdevice 34; a device type of the device 34, such as an instance of aparticular model of devices 34 (e.g., a brand or model of a mobilephone); and a device class of the device 34, such as the generalproperties and/or capabilities of the device 34 (e.g., an identificationof a device 34 as a mobile phone).

As a third variation of this first aspect, many types of associations 32may be utilized to associate a device 34 with an individual 14. As afirst example, the association 32 may be identified based on theproximity of the device 34 to the individual 14 (e.g., detecting thedevice 34 on the person of or in a close proximity with the individual14. As a second example, the association 32 may be identified based onan explicit association; e.g., in addition to its device identifier 36,the device 34 may identify the individual 14 who is authorized to usethe device 34. As a third example, the association 32 may be identifiedbased on correlation; e.g., even if the device 34 is not proximate tothe individual 14, the correlated presence and absence of the device 34and the individual 14 at similar times may indicate the association 32.For example, the device 34 may comprise a vehicle driven by theindividual 14, and even if the computer 12 may be unable to identify theoccupants of the vehicle (e.g., the device detector 40 may comprise alicense plate reader that monitors entry and exit of automobiles from aparking lot), the computer 12 may be able to correlate the presence of adevice 34 with the presence of one or more individuals 14 (e.g.,individuals 14 who are often detected at the same times that the vehicleis present in the parking lot), and therefore identify an association 32of the device 34 with the individual 14. As a fourth example, theassociation 32 may also be sporadic (e.g., an individual 14 mayoccasionally carry the device 34) and/or consistent (e.g., an implantedmedical device), and the computer 12 may accordingly adjust theevidentiary weight of the detected presence or absence of the device 34while identifying the individual 14. As a fifth example, an individual14 may also have associations with two or more devices 34, either in thealternative (e.g., an individual 14 may drive one of two vehicles) orcumulative (e.g., the individual 14 often carries two or more devices34, either independently or together). As a sixth example, a device 34may be shared by two or more individuals 14, and may therefore beassociated with both individual identities 16.

As a fourth variation of this first aspect, these techniques mayidentify an individual 14 with the cooperation of the individual (e.g.,a user who wishes to login to the computer 12) or without thecooperation of the individual (e.g., a hostile prisoner). Thesetechniques may also be utilized overtly (e.g., by notifying theindividual 14 of the automated identification, and possibly includingthe cooperation of the individual 14) or covertly (e.g., byclandestinely positioning the biometric detector 22 and/or the devicedetector 30 in a concealed location or at a long range from theindividual 14). Those of ordinary skill in the art may devise manyscenarios wherein the techniques presented herein may be utilized.

A second aspect that may vary among embodiments of these techniquesrelates to the registration of the individual identity 16 of anindividual 14 comprising at least one biometric 18 and at least onedevice identifier 36. As a first variation of this second aspect, thecomputer 12 may receive biometrics 18 and/or device identifiers 36 fromanother source, and may subsequently utilize this data as individualidentities 16 to identify individuals 14. Alternatively, the computer 12may also be configured to generate individual identities 16, e.g., uponreceiving an individual identity registration request to register theindividual identity 16 of the individual 14 (from the individual 14and/or another user of the computer 12). As a first such example, thecomputer 12 may passively wait until such a registration request isreceived, and may then activate the biometric detector 22 and/or thedevice detector 40 to detect the biometrics 18 and/or device identifiers36 of the device 34. As a second such example, the computer 12 mayspontaneously initiate the registration of an individual identity 16 ofan individual 14, e.g., by sporadically or continuously detectingbiometrics 18 and/or device identifiers 36, and initiating theregistration upon identifying a biometric 18 and/or device identifier 36that is not associated with an individual identity 16 in the data store78 (e.g., by searching the data store 78 upon detecting the biometric 18or device identifier 36, and upon not finding a corresponding individualidentity 16, storing a new individual identity 16 in the data store 78).This registration process may be initiated automatically, and/or may becontingent upon the consent of the individual 14 or another user (e.g.,the computer 12 may present to the individual 14 an offer to generate anindividual identity 16 for the individual 14 and/or to associate anidentified device 34 with the individual identity 16 of the individual14, and only proceeding upon receiving from the individual 14 anacceptance of the offer). As a third such example, the computer 12 mayseparately scan for unrecognized devices 34, and may seek to associatenewly detected devices 34 with one or more individuals 14; e.g., upondetecting a device identifier 36 and failing to find an individualidentity 16 in the data store 78 associated with the device identifier36, the computer 12 may ask one or more individuals 14 having anindividual identity 16 (e.g., the individuals 14 who are currentlypresent) to claim the device 34, and may associate the device identifier36 of the device 34 with the individual identity 16 of any individual 14who sends a claim to the device 34 to the computer 12.

FIG. 6 presents an illustration of an exemplary scenario 100 featuringseveral such variations of this second aspect during the registration ofan individual identity 16 of an individual 14. In this exemplaryscenario 100, a computer 12 having a biometric detector 22, a devicedetector 40, and a data store 78 storing an individual identity set 20may detect a presence of an individual 14. First, the computer 12 maypresent to the individual 14 an offer to register the individualidentity 16 of the individual 14 using one or more biometrics 16, andmay only proceed with biometric registration upon receiving from theindividual 14 an acceptance of the offer. The computer 12 may thenactivate the biometric detector 22, perform an analysis 26 of a capturedrepresentation 24 of the individual 14, extract one or more biometrics18, and create a new individual identity 16 for the individual 14comprising the biometric(s) 18. Additionally, the computer 12 mayactivate the device detector 40, and may receive from a device 34associated with the individual 14 one or more device indicators 36. Thecomputer 12 may then present to the individual 14 an offer to associatethe device 34 (and, in particular, the device identifiers 36 of thedevice 34) with the individual identity 16 of the individual 14. Theindividual 14 may or may not wish to be associated with the device 34(e.g., the individual 14 may be in only temporary possession of a device34 of another individual 14). Upon receiving from the individual 14 anacceptance of the offer to associate the device 34 with the individual14, the computer 12 may store the device identifier 36 in the data store78 associated with the individual identity 16 of the individual 14. Inthis manner, the computer 12 may register the individual identity 16 ofthe individual 14 including both one or more biometrics 18 and one ormore device identifiers 36, based on the interaction, consent, andcooperation of the individual 14. Those of ordinary skill in the art maydevise many variations in the registration of the individual identity 16of the individual 14 in accordance with the techniques presented herein.

A third aspect that may vary among embodiments of these techniquesrelates to the identification of individuals 14 based on the biometrics18 and device identifiers 36 associated with various individualidentities 16 representing various individuals 14. As a first variationof this second aspect, the identification may begin upon request of theindividual 14 or another user of the computer 12 (e.g., the computer 12may deactivate the biometric detector 22 and/or device detector 40 untilreceiving a request to identify an individual 14). Alternatively, thecomputer 12 may utilize the biometric detector 22 and/or the devicedetector 40 to detect a presence of an individual 14 (e.g., a lightsensor configured to detect when an individual 14 steps in front of acamera), and may spontaneously initiate the identification of theindividual 14. A combination of these techniques may also be utilized;e.g., upon detecting a presence of an individual 14, the computer 12 maypresent to the individual 14 an offer to initiate an identificationprocess involving various biometrics 18. The computer 12 may also beconfigured to, upon identifying an individual 14, notify the individual14 that the identification has been achieved.

As a second variation of this third aspect, the detected biometrics 18and/or device identifiers 36 may be utilized in any order and/orcombination. As a first such example, the computer 12 may concurrentlydetect one or more biometrics 18 and one or more device identifiers 36,which may be generated in a different order based on variouscircumstances (e.g., based upon varying complexity of the analyses 26 ofrespective representations 24), and, upon receiving each suchidentifier, may incrementally whittle down the set of individuals 14having individual identities 16 in the individual identity set 20 thatcorrespond to the detected identifiers. This example may beadvantageous, e.g., by concurrently invoking all of the identificationcapabilities of the computer 12 in order to achieve a rapididentification of the individual 14. As a second such example, thecomputer 12 may utilize the detection of biometrics 18 and/or deviceidentifiers 36 in a particular order. For example, the computer 12 mayinvoke a sequence of identification by first detecting more accurate,more efficient, and/or more diagnostic identifiers, and maydifferentially select other identifiers to narrow down the subset ofindividual identities 16. As another such example, the computer 12 mayfirst detect a device identifier 36 of the individual 14. Uponidentifying only one individual identity 16 associated with the deviceidentifier 36, the computer 12 may identify one or more biometrics 18 ofthe individual 14 to verify this individual identity 16 (e.g., to verifythat another individual 14 is not simply carrying the device 34 ofanother individual 14). Alternatively, upon identifying two or moreindividuals 14 who are associated with the device 34 (e.g., two or moreindividual identities 16 comprising the device identifier 36 of thedevice 34), the computer 12 may use biometrics 16 to differentiating theindividuals 14 in order to identify the individual 14. For example, iftwo individuals 14 are associated with a particular device 34 that isdetected in association with an unidentified individual 14, the computer12 may endeavor to select a biometric 16 that efficiently and accuratelydifferentiates these two individuals 14. The computer 12 may then invokethe corresponding biometric detector 22 to detect the biometric 16 ofthe unidentified individual 14, and may therefore identify theunidentified individual 14 according to one (or neither) of theindividual identities 16. These examples may involve a longer duration(due to invoking identification techniques sequentially), but may reducethe invocation of some identifying components that may be unnecessary toidentify the individual 14, thereby conserving computing resourcesand/or the privacy of the individual 14 (e.g., by not utilizing apotentially sensitive biometric identification that may not benecessary).

As a third variation of this third aspect, a device detector 40 may havea variable device detector range (e.g., a Wi-Fi receiver may apply moreor less power to an antenna in order to increase or decrease the rangeof device detection, and/or may be configured only to detect devices 34within a certain proximity to the computer 12). The computer 14 maytherefore, upon receiving from an individual 14 a device detector range,accordingly set the device detector range of the device detector.

As a fourth variation of this third aspect, the computer 12 may includean individual whitelist, which may identify one or more individualidentities 16 that are to be searched before other individual identities16. For example, the computer 12 may be more often patronized (or thearea of the detection may be more heavily frequented) by a particularsubset of individuals 14 among the set of individuals 14 for which thecomputer 12 has individual identities 16. The computer 12 may thereforepresent an improved user experience by, upon detecting one or morebiometrics 18 of an unidentified individual 14, first searching theindividual identities 16 referenced by the individual whitelist, inorder to recognize such individuals 14 faster; and may only search theother individual identities 16 if the detected biometrics 18 and/ordevice identifiers 36 are not associated with any individual identity 16included in the individual whitelist.

As a fifth variation of this third aspect, the computer 12 may include adevice blacklist, which may identify one or more blacklisted devicesthat are not to be identified and/or utilized in the identification ofany individual 14. For example, a device 16 may publicly or communallyutilized (e.g., a portable device that is available to many or anyindividual 14 in a public space), and while the identification of otherdevices 16 may promote the identification of associated individuals 14,the presence of this particular device 16 may only reduce the accuracyof the identification of individuals 14. Accordingly, when the computer12 detects one or more device identifiers 36, the computer 12 may searchthe device blacklist, and may only include in the identification processdevice identifiers 36 that are not identified in the device blacklist.Those of ordinary skill in the art may devise many variations in theidentification of individuals 14 using biometrics 18 and deviceidentifiers 36 in accordance with the techniques presented herein.

A fourth aspect that may vary among embodiments of these techniquesrelates to tracking the presence of an individual 14 who has beenidentified using biometrics 18 and device identifiers 36. An individual14 may be identified as present in a particular area (e.g., a proximityto the computer 12, one or more biometric detectors 22, and/or one ormore device detectors 40). However, as the individual 14 moves aboutwithin the area, the identification of the individual 14 using thesecomponents may fluctuate. Nevertheless, it may be possible to track thepersistent presence of the individual 14 within the area using thecontinued detection of biometrics 18 and/or device identifiers 36.

FIG. 7 presents an illustration of an exemplary scenario 110 featuring apersistent tracking of the presence of various individuals 14 in anarea. In this exemplary scenario 110, a computer 12 features anindividual identity set 20 comprising a set of individual identities 16identifying respective individuals 14 according to a biometric 18 and adevice identifier 36. The computer 12 is also equipped with a camera(operating as a biometric detector 22) and a wireless communicationreceiver, such as a Wi-Fi network adapter (operating as a devicedetector 40). Moreover, these detectors have different ranges ofdetection; e.g., the biometric detector 22 may have a biometric detectorrange 112 within which biometrics 18 may be detected (e.g., the line ofsight and degree of panorama of the camera), while the device detector40 may detect devices 34 within a broad device detector range 114 (e.g.,an entire radius of the device detector 40, regardless of line ofsight).

Within the area of the exemplary scenario 110 of FIG. 7, the computer 12may endeavor to detect and identify various individuals 14. For example,when a presence of a first individual 14 within the biometric detectorrange 112 is detected by the camera, biometrics 18 and deviceidentifiers 36 may be collected, and the first individual 14 may beidentified as the individual having the name “Drew Stone.” Additionally,a “visible” presence indicator 116 may be recorded in the individualidentity 16 of this individual 14 to indicate that the individual 14 iscurrently present and visible to the computer 12 within the area.However, the individual 14 may eventually move to a different locationwithin the area, and the computer 12 may no longer detect the biometrics18 of the individual within the biometric detector range 112. However,the persistent presence of the individual 14 may continue to be trackeddue to the continued detection of the device identifier 24. For example,a second individual 14 may initially appeared within the biometricdetector range 112, and may have been identified as the individual named“Mike Jones,” but the individual 14 may have since moved to a differentarea. Nevertheless, the device identifier 36 of a device 34 carried bythe second individual 14 may continue to be detected within the devicedetector range 114 of the device detector 40. Accordingly, in theindividual identity set 20, the “visible” presence indicator 116 storedin the individual identity 16 of this individual 14 may be replaced witha “present” presence indicator 116 to indicate that, although theindividual 14 is no longer visible to the camera of the computer 12, thepresence of the individual 14 in the area is still detected. Finally,when an individual 14 who has been previously identified (such as athird individual 14 having the name “Kim Davis”) departs both thebiometric detector range 112 and the device detector range 114, thecomputer 12 may replace the “present” presence indicator 116 in theindividual identity 16 of this individual with an “absent” presenceindicator 116 to indicate the departure of the individual 14 from thearea. In this manner, the various detectors of the computer 12 mayinteroperate to track the persistence presence of individuals 14 in anarea despite the fluctuating visibility of such individuals 14 to thebiometric detector(s) 22 and/or the device detector(s) 40. Those ofordinary skill in the art may devise many ways of tracking the presenceof identified individuals 14 in accordance with the techniques presentedherein.

Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of implementing the claims.

As used in this application, the terms “component,” “module,” “system”,“interface”, and the like are generally intended to refer to acomputer-related entity, either hardware, a combination of hardware andsoftware, software, or software in execution. For example, a componentmay be, but is not limited to being, a process running on a processor, aprocessor, an object, an executable, a thread of execution, a program,and/or a computer. By way of illustration, both an application runningon a controller and the controller can be a component. One or morecomponents may reside within a process and/or thread of execution and acomponent may be localized on one computer and/or distributed betweentwo or more computers.

Furthermore, the claimed subject matter may be implemented as a method,apparatus, or article of manufacture using standard programming and/orengineering techniques to produce software, firmware, hardware, or anycombination thereof to control a computer to implement the disclosedsubject matter. The term “article of manufacture” as used herein isintended to encompass a computer program accessible from anycomputer-readable device, carrier, or media. Of course, those skilled inthe art will recognize many modifications may be made to thisconfiguration without departing from the scope or spirit of the claimedsubject matter.

FIG. 8 and the following discussion provide a brief, general descriptionof a suitable computing environment to implement embodiments of one ormore of the provisions set forth herein. The operating environment ofFIG. 8 is only one example of a suitable operating environment and isnot intended to suggest any limitation as to the scope of use orfunctionality of the operating environment. Example computing devicesinclude, but are not limited to, personal computers, server computers,hand-held or laptop devices, mobile devices (such as mobile phones,Personal Digital Assistants (PDAs), media players, and the like),multiprocessor systems, consumer electronics, mini computers, mainframecomputers, distributed computing environments that include any of theabove systems or devices, and the like.

Although not required, embodiments are described in the general contextof “computer readable instructions” being executed by one or morecomputing devices. Computer readable instructions may be distributed viacomputer readable media (discussed below). Computer readableinstructions may be implemented as program modules, such as functions,objects, Application Programming Interfaces (APIs), data structures, andthe like, that perform particular tasks or implement particular abstractdata types. Typically, the functionality of the computer readableinstructions may be combined or distributed as desired in variousenvironments.

FIG. 8 illustrates an example of a system 130 comprising a computingdevice 132 configured to implement one or more embodiments providedherein. In one configuration, computing device 132 includes at least oneprocessing unit 136 and memory 138. Depending on the exact configurationand type of computing device, memory 138 may be volatile (such as RAM,for example), non-volatile (such as ROM, flash memory, etc., forexample) or some combination of the two. This configuration isillustrated in FIG. 8 by dashed line 134.

In other embodiments, device 132 may include additional features and/orfunctionality. For example, device 132 may also include additionalstorage (e.g., removable and/or non-removable) including, but notlimited to, magnetic storage, optical storage, and the like. Suchadditional storage is illustrated in FIG. 8 by storage 140. In oneembodiment, computer readable instructions to implement one or moreembodiments provided herein may be in storage 140. Storage 140 may alsostore other computer readable instructions to implement an operatingsystem, an application program, and the like. Computer readableinstructions may be loaded in memory 138 for execution by processingunit 136, for example.

The term “computer readable media” as used herein includes computerstorage media. Computer storage media includes volatile and nonvolatile,removable and non-removable media implemented in any method ortechnology for storage of information such as computer readableinstructions or other data. Memory 138 and storage 140 are examples ofcomputer storage media. Computer storage media includes, but is notlimited to, RAM, ROM, EEPROM, flash memory or other memory technology,CD-ROM, Digital Versatile Disks (DVDs) or other optical storage,magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, or any other medium which can be used to storethe desired information and which can be accessed by device 132. Anysuch computer storage media may be part of device 132.

Device 132 may also include communication connection(s) 146 that allowsdevice 132 to communicate with other devices. Communicationconnection(s) 146 may include, but is not limited to, a modem, a NetworkInterface Card (NIC), an integrated network interface, a radio frequencytransmitter/receiver, an infrared port, a USB connection, or otherinterfaces for connecting computing device 132 to other computingdevices. Communication connection(s) 146 may include a wired connectionor a wireless connection. Communication connection(s) 146 may transmitand/or receive communication media.

The term “computer readable media” may include communication media.Communication media typically embodies computer readable instructions orother data in a “modulated data signal” such as a carrier wave or othertransport mechanism and includes any information delivery media. Theterm “modulated data signal” may include a signal that has one or moreof its characteristics set or changed in such a manner as to encodeinformation in the signal.

Device 132 may include input device(s) 144 such as keyboard, mouse, pen,voice input device, touch input device, infrared cameras, video inputdevices, and/or any other input device. Output device(s) 142 such as oneor more displays, speakers, printers, and/or any other output device mayalso be included in device 132. Input device(s) 144 and output device(s)142 may be connected to device 132 via a wired connection, wirelessconnection, or any combination thereof. In one embodiment, an inputdevice or an output device from another computing device may be used asinput device(s) 144 or output device(s) 142 for computing device 132.

Components of computing device 132 may be connected by variousinterconnects, such as a bus. Such interconnects may include aPeripheral Component Interconnect (PCI), such as PCI Express, aUniversal Serial Bus (USB), firewire (IEEE 1394), an optical busstructure, and the like. In another embodiment, components of computingdevice 132 may be interconnected by a network. For example, memory 138may be comprised of multiple physical memory units located in differentphysical locations interconnected by a network.

Those skilled in the art will realize that storage devices utilized tostore computer readable instructions may be distributed across anetwork. For example, a computing device 150 accessible via network 148may store computer readable instructions to implement one or moreembodiments provided herein. Computing device 132 may access computingdevice 150 and download a part or all of the computer readableinstructions for execution. Alternatively, computing device 132 maydownload pieces of the computer readable instructions, as needed, orsome instructions may be executed at computing device 132 and some atcomputing device 150.

Various operations of embodiments are provided herein. In oneembodiment, one or more of the operations described may constitutecomputer readable instructions stored on one or more computer readablemedia, which if executed by a computing device, will cause the computingdevice to perform the operations described. The order in which some orall of the operations are described should not be construed as to implythat these operations are necessarily order dependent. Alternativeordering will be appreciated by one skilled in the art having thebenefit of this description. Further, it will be understood that not alloperations are necessarily present in each embodiment provided herein.

Moreover, the word “exemplary” is used herein to mean serving as anexample, instance, or illustration. Any aspect or design describedherein as “exemplary” is not necessarily to be construed as advantageousover other aspects or designs. Rather, use of the word exemplary isintended to present concepts in a concrete fashion. As used in thisapplication, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or”. That is, unless specified otherwise, or clearfrom context, “X employs A or B” is intended to mean any of the naturalinclusive permutations. That is, if X employs A; X employs B; or Xemploys both A and B, then “X employs A or B” is satisfied under any ofthe foregoing instances. In addition, the articles “a” and “an” as usedin this application and the appended claims may generally be construedto mean “one or more” unless specified otherwise or clear from contextto be directed to a singular form.

Also, although the disclosure has been shown and described with respectto one or more implementations, equivalent alterations and modificationswill occur to others skilled in the art based upon a reading andunderstanding of this specification and the annexed drawings. Thedisclosure includes all such modifications and alterations and islimited only by the scope of the following claims. In particular regardto the various functions performed by the above described components(e.g., elements, resources, etc.), the terms used to describe suchcomponents are intended to correspond, unless otherwise indicated, toany component which performs the specified function of the describedcomponent (e.g., that is functionally equivalent), even though notstructurally equivalent to the disclosed structure which performs thefunction in the herein illustrated exemplary implementations of thedisclosure. In addition, while a particular feature of the disclosuremay have been disclosed with respect to only one of severalimplementations, such feature may be combined with one or more otherfeatures of the other implementations as may be desired and advantageousfor any given or particular application. Furthermore, to the extent thatthe terms “includes”, “having”, “has”, “with”, or variants thereof areused in either the detailed description or the claims, such terms areintended to be inclusive in a manner similar to the term “comprising.”

What is claimed is:
 1. A method of identifying individuals using acomputer having a processor, a data store, a biometric detector, and adevice detector, the method comprising: executing on the processorinstructions that cause the computer to: for respective individuals,store in the data store a biometric association of an individualidentity and a biometric measurement exhibited by the individual; whiledetecting, during a first individual presence of the individual, a firstdevice presence of a device that is identifiable by a device identifier,storing in the data store a device association of the device identifierof the device and the individual identity of the individual, where thedevice association indicates that possession of the device having thedevice identifier establishes the individual identity of the individual;and during a second presence of an individual in possession of anindividual device, identify the individual by: using the biometricdetector, detecting a biometric measurement exhibited by the individual;using the device detector, identifying the device identifier of thedevice in the possession of the individual; determining, according tothe biometric association and the device association stored in the datastore, that the individual exhibiting the biometric measurementassociated with the individual identity, and also in possession of thedevice identified by the device identifier associated with theindividual identity, establishes the individual identity of theindividual; and subsequently interacting with the individual accordingto the individual identity established according to the biometricmeasurement and the device identifier.
 2. The method of claim 1: thebiometric detector comprising a face detection component; and at leastone biometric measurement associated with the individual comprising aface biometric measurement.
 3. The method of claim 1: at least onedevice associated with at least two individuals; and the instructionsconfigured to, while detecting an individual and upon identifying twoindividual identities that are associated with a device identifier ofthe device: identify at least one biometric measurement differentiatingthe individual identities; and using the biometric detector, detectingthe biometric measurement associated with the individual.
 4. The methodof claim 1, storing the individual identity in the data storecomprising: storing the individual identity in the data store uponreceiving an individual identity registration request to register theindividual identity.
 5. The method of claim 4, storing the individualidentity in the data store comprising: upon the biometric detectordetecting a biometric measurement, search the data store for anindividual identity comprising the biometric measurement; and upon notfinding in the data store an individual identity comprising thebiometric measurement, storing the individual identity in the datastore.
 6. The method of claim 4, storing the individual identitycomprising: searching for devices that are detectable by the computer;and upon receiving at least one device identifier identifying the deviceduring the first individual presence of the individual, storing thedevice identifier in the data store associated with the individual. 7.The method of claim 6: the device comprising at least one user interfaceconfigured to communicate with the individual; and storing the deviceidentifier in the data store comprising: presenting to an individualthrough the user interface an offer to associate the device with theindividual identity of the individual; and upon receiving from theindividual through the user interface an acceptance of the offer,storing the device identifier in the data store associated with theindividual identity of the individual.
 8. The method of claim 1, theinstructions configured to: upon detecting a device identifier, searchthe data store for an individual identity comprising the network adapteridentifier; and upon failing to find in the data store an individualidentity comprising the device identifier: ask at least one individualhaving an individual identity to claim the device; and upon receivingfrom an individual having an individual identity a claim of the device,associating the device identifier identifying the device with theindividual identity of the individual in the data store.
 9. The methodof claim 1, the device configured to detect the at least one biometricmeasurement of an individual upon detecting the first individualpresence of the individual.
 10. The method of claim 9: the devicecomprising at least one user interface configured to communicate withthe individual; and the instructions configured to: upon detecting anindividual using the biometric measurement, present to the individualthrough the user interface an offer to identify the individual; and uponreceiving from the individual through the user interface an acceptanceof the offer to identify the individual, detect the at least onebiometric measurement of the individual.
 11. The method of claim 1: thedevice detector having a variable device detector range; and theinstructions configured to, upon receiving from an individual a devicedetector range, set the device detector range of the device detector.12. The method of claim 1: the computer comprising a device blacklistidentifying the device identifier of at least one blacklisted device;and receiving the at least one device identifier identifying the devicefurther comprising: responsive to determining that the device isidentified as a blacklisted device in the device blacklist, refrainingfrom identifying the identifying the individual according to theblacklisted device.
 13. The method of claim 1: the device comprising anindividual whitelist identifying the device identifier of at least onedevice that is associated with at least one whitelisted individual; andretrieving an individual identity from the data store comprising:searching the individual identities from the data store to retrieve anindividual identity of at least one whitelisted individual that isassociated with the device identifier of the device; and upon failing tofind in the data store an individual identity of any whitelistedindividual in the individual whitelist that is associated with thedevice identifier of the device, refraining from identifying theindividual according to the device identifier.
 14. The method of claim1, the instructions configured to, upon retrieving from the data storean individual identity comprising the biometric measurement and thedevice identifier, notifying the individual associated with theindividual identity of an identification of the individual.
 15. Themethod of claim 1, the instructions configured to, upon identifying anindividual identity of an individual associated with a biometricmeasurement detected by the biometric detector and a device identifierdetected by the device detector, store in the data store a detectedindicator associated with the individual in the data store.
 16. Themethod of claim 15, the instructions configured to, after storing adetected indicator associated with the individual in the data store andupon not detecting a biometric measurement identifying the individual,replace the detected indicator with a present indicator associated withthe individual in the data store.
 17. The method of claim 16, theinstructions configured to, after storing a present indicator associatedwith the individual in the data store and upon not detecting a deviceidentifier identifying a device associated with the individual, replacethe present indicator with an absent indicator associated with theindividual in the data store.
 18. The method of claim 1, wherein thedevice identifier is selected from a network adapter set comprising: acellular network adapter; a local area network adapter; and a wide areanetwork adapter.
 19. A system for identifying individuals using a servercomprising a processor, a data store, a biometric detector, and a devicedetector, the system comprising: a memory storing instructions that,when executed by the processor, provide a system comprising: anindividual identity generator that: for respective individuals, storesin the data store a biometric association of an individual identity anda biometric measurement exhibited by the individual; and whiledetecting, during a first individual presence of the individual, a firstdevice presence of an individual device that is identifiable by a deviceidentifier, storing in the data store a device association of the deviceidentifier of the individual device with the individual identity of theindividual in the data store, where the device association indicatesthat possession of the device having the device identifier establishesthe individual identity of the individual; and an individual identifierthat, during a second presence of an individual and in possession of anindividual: using the biometric detector, detects a biometricmeasurement exhibited by the individual; using the device detector,identifies the device identifier of the individual device in thepossession of the individual; determines, according to the biometricassociation and the device association stored in the data store, thatthe individual exhibiting the biometric measurement associated with theindividual identity, and also in possession of the device identified bythe device identifier associated with the individual identity,establishes the individual identity of the individual; and subsequentlyinteracts with the individual according to the individual identityestablished according to the biometric measurement and the deviceidentifier.
 20. A server that identifies individuals using biometricmeasurements and device identifiers, the server comprising: a processor;a data store; a biometric detector; a device detector; and a memorystoring instructions that, when executed by the processor, cause theserver to: for respective individuals, store in the data store abiometric association of an individual identity and a biometricmeasurement exhibited by the individual; and while detecting, during afirst individual presence of the individual, a first device presence ofan individual device that is identifiable by a device identifier, storein the data store a device identifier association of the deviceidentifier of the individual device with the individual identity of theindividual in the data store, where the device association indicatesthat possession of the device having the device identifier establishesthe individual identity of the individual; and during a second presenceof an individual in possession of an individual device, identify anidentity of the individual by: using the biometric detector, detecting abiometric measurement exhibited by the individual; using the devicedetector, identifying a device identifier of the individual device inthe possession of the individual; determining, according to thebiometric association and the device association stored in the datastore, that the individual exhibiting the biometric measurementassociated with the individual identity, and also in possession of thedevice identified by the device identifier associated with theindividual identity, establishes the individual identity of theindividual; and subsequently interacting with the individual accordingto the individual identity established according to the biometricmeasurement and the device identifier.